This project is an extensions of previous work involving the creation, development and testing of image processing techniques designed to improve diagnostic performance. Current work has centered on methods relevant to the processes of radiologic image subtraction and tomosynthesis. Work continues in the area of automated manipulation to facilitate registration. Particular emphasis has been placed on the selection of task-specific measures of image similarity and the use of warping transformations to facilitate restoration of projective distortions. Other related research involved the development and testing of an algorithm designed to estimate the area and volume of localized osseous lesions from dental radiographs. Preliminary data correlate well with measurements of size and volume measured directly on radiologic phantoms. Three different approaches have been studied which reduce unwanted blur produced by tomosynthetic reconstruction from radiographic images. One involves iterative correction of a given tomosynthetic plane made possible by selective subtraction of blurred signals produced by convolution of structures in that plane and kernels determined by the exposure geometry. The other two are simple deconvolution techniques, one linear and the other nonlinear. The interative method was shown to converge to a set known to contain the correct solution even in the presence of uncorrelated noise. However, it also was found to be expensive computationally. Psychophysical analysis of the simpler deconvolution techniques indicate that they can produce images which rival those processed iteratively in certain dental applications. Considered en masse these findings are consistent with capabilities proposed for a prototype x-ray system currently being developed in cooperation with scientists at the National Bureau of Standards.